1. Evolutionary Ecology of Movement Definitions and confusion –Taxes and Kineses –Migration vs Dispersal –Discrete vs. Continuous variation –Philopatry? Evolution –Migratory polymorphisms –Macroptery and habitat persistence Ecology –Metapopulation persistence –Movement response to habitat change

2. People break their teeth knowing a great deal about what’s easy, or at least possible, to measure

3. Simple movement patterns Taxes –Movements directed towards a goal or stimulus Kineses –Undirected movements (not oriented to a landmark or stimulus) Speed Frequency of turns Angles of turns –Kineses can yield insight on perceptions and state

4. Migration vs. Dispersal Migration –Behavioral –Physiological Dispersal –Ecological

5. Migration vs. Dispersal Migration –Behavioral Straightened out directed movement Inhibition of responses to normal stopping cues

7. The Art of Falconry, Frederick II Hohenstaufen, 1250

8. Migration vs. Dispersal Migration –Behavioral Straightened out directed movement Inhibition of responses to normal stopping cues –Physiological Accumulation and mobilization of resources –Fat as fuel

9. Fruit is good fuel Parrish 1997, migrants in RI

10. Cost of fat in sedge warblers Fuel load = % of lean mass in stored fat

11. Migration vs. Dispersal Dispersal –Ecological Increase in the average distance among individual –Opposite of aggregation Movement away from… –Relatives –Population or patch –Competitors  TOWARDS… oSuperior habitat oUnoccupied habitat oMates Movement from natal to breeding site or between breeding sites

12. Dispersal Definitions Qualitative –Emigration From family From a population or patch –Opposite of philopatry –Costs of leaving or joining a group or territory fitness of residents vs immigrants Quantitative –How far do individuals move? –Do individuals disperse the shortest possible distance Relative philopatry –What is the cost of increased time or distance moved?

13. Movement is hard to study Where’d they go? They’re gone? I see one over there Is it philopatric?

14. Philopatry? Waser 1985

15. Competition? Waser 1985 Ecology 66:1170 Models based on Murray 1967 Hypothesis that animals move the shortest distance possible

16. The Problem Dispersal is often measured using mark- recapture on limited study areas The farther an animal travels, the less likely it is to remain on the study area

17. Dispersal and survival Survival estimates depend critically on dispersal estimates –Individuals who disappear may have died or dispersed

18. Wrentit (Chamea fasciata)

19. The palomarin study area

20. Observed and corrected dispersal distributions Baker, Guepel and Nur, 1995

21. Mean dispersal estimate increased from 268 to 379 meters Juvenile survival estimate increased from 7.3% to 22.9 % Be critical of any claims of philopatry

22. How can correction methods be tested? Subsample large study areas, for a start –Cooper et al. used large area from red- cockaded woodpecker –Corrction method worked ok to estimate median movement, less well on variance, survival

23. Larger study areas have higher ‘survival’ Zimmerman et al 2007 J App Ecol 44:963-971

24. Larger study areas have lower ‘emigration’ Zimmerman et al 2007 J App Ecol 44:963-971

25. Evolution of migration Migratory polymorphisms –Migratory vs. Sedentary morphs

26. Garden fleahopper

28. Migratory syndromes Morphology –Long wings and flight muscles Behavior –Motivation to fly vs. mate Correlated traits –Diapause, plasticity, body size, other life history Polymorphism or Polyphenism

29. Megoura viciae, Gilbert “Developmental Biology

30. Endocrine mechanisms AJ Zera 2003

31. Endocrine mechanisms AJ Zera 2003

32. Migratory Tradeoffs Long wings and flight muscles are costly Time spent in migration or migratory condition Mortality during migration –Fitness costs Age at first reproduction delayed Total fecundity reduced

33. How do migrants persist?

35. Trade off between extinction and colonization R. Denno, in D. Roff 1994, Am Nat 144:772- 798

36. Ecology of dispersal

37. Persistence of metapopulations Metapopulation - a collection of smaller populations –Each individual population is likely to go extinct –Recolonization from other populations allows the population as a whole to persist

40. Persistence depends on dispersal between subpopulations –Survival during dispersal –Willingness to disperse between populations Leave good habitat Cross over poor habitat How are corridors treated?

41. Questions How will movement change when habitat changes –Immediate behavior –Evolved response How does migration evolved in fragmented habitat How does migration evolve over the course of invasions?

42. Hemilepistus reaumuri

43. Desert isopods are detritivores They feed on soil crust,...and dead plants

44. Every Spring

45. Investigating a burrow

46. A new burrow

47. Optimality models State space –Current site quality, P(successful reproduction) Strategy set –Remain in current site, or leave to search Optimization criterion –Family survival to following year Constraints –Search and sampling times, range of habitat quality, mortality during searching

48. Dynamic programming models Dynamic programming is able to solve problems when fitness depends on timing of actions. Dynamic programming models work backwards from the final time period, so that the later consequences of decisions made now are always known

49. Why dynamic programming? Day 11020 Habitat quality

50. Fitness if settled Day 11020 Habitat quality Survive to end of season Survive to reproduce

51. Fitness if dispersal continues Day 11020 Habitat quality

52. Why dynamic programming? Day 11020 Habitat quality

53. Why dynamic programming? Day 11020 Habitat quality To solve these questions, start at the end, work backwards Stay

54.  Fitness is defined as the probability of family survival for a given site quality, x  At every time t, is it better to stay or search? F is in terms of fitness at the end of the season, T

55.  The product of survival until the end of the season and the probability of family survival  is mortality, x is site quality, t and T are the present and final days of the season Fitness of settling

56.  The product of survival until the end of the season and the probability of family survival  is mortality, x is site quality, t and T are the present and final days of the season is the probability of ending up in a site of quality x i Fitness of searching

57. Habitat quality (probability of breeding) Settle in this range of habitat quality and time Search in this range of habitat quality and time 0 10 20 30 40.52 Time (days) 0 1.04 Be selective early on, even at good sites As the season’s end approaches, be less selective The distribution of habitat quality Model output: The optimal strategy for every combination of time and state

58. What does ‘dispersal cost’ an ‘suitable habitat’ mean? Data from 50 years pooled –Distribution of distance or time dispersed –Probability of settling as a function of time of season Proportion settled Time or distanceTime of season Probability of settling

59. Incremental costs favor less choosiness / greater philopatry Probability of settling Incremental cost No cost

60. Incremental costs create right skewed distributions Proportion settled Incremental cost No cost

61. What if sites are ‘suitable’ or ‘unsuitable’? Probability of settling 0 0.2 0.4 0.6 0.8 1 0510152025303540 Distance or day of season Incremental cost No cost Habitat divided into suitable and unsuitable sites

62. 0 0.05 0.1 0.15 0.2 0.25 0.3 0510152025303540 Distance or time traveled Proportion settled Incremental cost No cost Costs of dispersal only matters if habitat varies continuously Habitat divided into suitable and unsuitable sites

63. Simulation results Incremental costs greatly influence dispersal distances and population distributions If only ‘suitable’ and unsuitable sites exist, dispersers must keep moving –Suitable and unsuitable categories are fluid, depend on time of season and costs –Habitat quality is usually a continuous quantity

64. An evolutionary scenario Individuals are born with an innate likelihood of settling –Product of selection During dispersal, individuals use their experience to adjust their likelihood of settling –Is this a good year or bad?

65. Model conclusions Models with and without learning predict opposite responses to habitat decline –No learning: Dispersal will increase, as individuals search for sites they are unlikely to find –Learning: A decline in habitat will lead to a reduction in selectivity and willingness to search, and no change or a reduction in dispersal

66. Two examples of dispersal evolution In fragmented habitat –Pararge aegeria in woodland and farmlands Over the course of an invasion –Frequency of macroptery in bush-crickets

67. Two examples of dispersal evolution In fragmented habitat –Pararge aegeria (speckled wood) in woodland and farmlands Merckx et al 2003 ProcB

68. Pararge aegeria dispersal Females collected from mostly forested areas and from primarily agricultural and developed areas Reared offspring on cage plants, released adults into 8*30 m outdoor cages –Cages had meadow and woody areas Does fragmentation select for increased movement? –Because resources are more scattered –Because of metapopulation benefits Does fragmentation select for reduced movement? –Because of costs of flying through open areas –Because of costs of changing habitat Merckx et al 2003 ProcB

69. Pararge aegeria dispersal (Open bars warm, filled bars cold)

70. Bush cricket movement during and following invasion Predictions –Initial founder populations –Subsequent evolution –Think of Roff 1994 Simmons and Thomas 2004 Am Nat

71. Bush cricket movement during and following invasion Simmons and Thomas 2004 Am Nat © 2003 Troy Bartlett

72. Simmons and Thomas 2004 Am Nat

73. Older populations have fewer macropters

74. Simmons and Thomas 2004 Am Nat Core populations have fewer macropters Simmons and Thomas 2004 Am Nat

75. Core populations have shorter flight range

76. Tracking butterfly movements with harmonic radar Ovaskainen O. et.al. PNAS 2008;105:19090-19095 ©2008 by National Academy of Sciences

77. Butterflies from new populations traveled farther than those from old (large or small) populations

78. Take home messages The best movement work is on butterflies? Evolution of migration is always a multilevel process –Local and short term fitness measures are deceptive Data shapes theory –Costs are hard to measure Especially incremental ones –Quantitative distances are hard to measure –Lots of work on ‘dispersal’ never looks at continuous incremental variation in movement

79. Data shapes theory Study areas are limited, static Qualitative questions are much more studied, better understood, than quantitative ones Can technology help? Movebank / Icarus / ARTSI? What is the value of our slice of science? Understanding of behavior, rather than application Current events